Which machine learning algorithms does FaceApp use

The best apps made successful by AI and machine learning

The market is saturated with apps: every niche seems to be already occupied and apps for every taste and inclination. If you want to develop an app today that offers the user real added value, you need an expanded understanding of what the user actually wants. The ideal way to do this is "machine learning", i.e. learning from the behavior of the user.

In this article you will find four mobile applications that have only become what they are today through "machine learning". Let this inspire you when developing your app.

Basic concepts of AI and machine learning

Before we begin, let's briefly define the basic terms.

  • Artificial intelligence (AI) or AI (artificial intelligence) - intelligence generated by programs on computers.
  • Machine Learning - Algorithms that recognize patterns in the same way as human brains. In contrast to working through hard-coded rules, this approach is based on gaining experience in order to answer questions better and better.
  • Neural network - a group of software and hardware mimicking the work of the human brain. It is organized in layers of interconnected artificial neurons; Each shift can be taught to prioritize criteria over others based on its weights.
  • Deep learning - a way to train neural networks to solve problems for which they were not programmed.
  • Linear regression - a machine learning algorithm based on the linear relationship between the input variables and an output variable.
  • Logistic regression - a machine learning algorithm used to model a binomial result with one or more explanatory variables.

Phew that was a chunk of dry information; now it's getting more entertaining:

Machine learning in Netflix

Netflix is ​​one of the most obvious examples of machine learning in mobile apps. Practically everyone knows it today. Why? Because Netflix knows what you want to watch before you want to watch it! A few decades ago it could have been thought of as magical. As we know, the magic behind this trick is machine learning.

Netflix has grown from a DVD rental website to a global streaming service. And the majority of the success story has to do with machine learning! Netflix uses linear regression, logistic regression, and other machine learning algorithms. All of those creepy words mean that Netflix has perfected its personalized recommendations using ML.

Netflix's content is broken down by genre, actor, reviews, length, year, and more. All of this data flows into machine learning algorithms. Machine learning not only means establishing obvious connections, but also drawing conclusions that a human observer would miss.

The same thing happens if you only watch one trailer, give a bad review, or if you choose the seventh recommendation instead of the first. Machine learning algorithms adapt to the behavior of a user to provide extremely personalized content.

Tinder - fire and flame for AI

Everyone knows it as a dating app that shows you singles in the area. To find a perfect match, Tinder uses all kinds of love spells and potions and one of them is machine learning. The potion is called "Smart Photos" and increases a user's chances of finding a match.

With the help of machine learning, this feature shows a random order of your profile photos for people and analyzes how often they have been swiped right or left. This knowledge enables Tinder to reorder your photos by putting the most popular ones first. This system is constantly evolving and the level of improvement depends on the input - the more the better.


Snapchat

The most common application of machine learning is the evaluation of "big user data" in order to generate an individualized recommendation for the user from the behavior of many. But there are other machine learning skills as well.

Snapchat's filters are a fantastic combination of augmented reality and machine learning algorithms for image recognition.

How do Snapchat filters work?

The first step is to recognize a face. The program sees a photo as a set of data for the color value of each individual pixel. But how does he know which part of the picture is a face?

The algorithm searches for areas of contrast between light and dark parts of the image. By repeatedly scanning the image data that calculates the difference between the grayscale pixel values ​​under the white boxes and the black boxes, the program can detect faces.

For example, the bridge of the nose is usually lighter than the surrounding area on either side, the eye sockets are darker than the forehead, and the center of the forehead is lighter than its sides. However, this type of algorithm only recognizes frontal faces.

However, in order to put the infamous corolla on the user, the app has to do more than just recognize a face. It has to locate facial features. According to the patterns, this is done with an "active shape model" - a statistical model of a face shape trained by people who manually marked the boundaries of facial features on hundreds of sample images. The algorithm takes an average face from this trained data and aligns it to the image from your phone's camera, scales it, and rotates it accordingly where it already knows your face is. But it doesn't fit perfectly, so the model analyzes the pixel data around each of the points and looks for edges defined by lightness and darkness.

Once Snapchat locates your facial features, these points are used as coordinates to create a mesh - a 3D mask that can move, rotate, and scale with your face once the video data is available for each image. You can do a lot with it. You can deform the mask to change your face shape, change your eye color and accessories, and set animations that trigger when you open your mouth or move your eyebrows.

Google Maps

But there are also very tangible and practical applications that would not be possible without machine learning.

The Google researchers collected and studied data from over 100,000 people. They asked questions like "How long did it take you to find a parking space?" To create training models, they used the anonymous information collected from users who chose to share their location data. I'm one of those people, so when I start going around in circles after getting to my destination, I obviously have trouble finding a parking space in a certain area.

Then, using a standardized logistic regression model, the app uses the functions based on the dispersion of parking locations and predicts when, where and how difficult it will be to find an empty space.

So if you click on the "Find a parking space" tab on the route map, you will see a list of parking spaces in your area and directions to your destination. It's just a shame that this function is currently only available in some major US cities.




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